每日简报

利用因果图识别自相关时间序列中的弹性AI 解读

The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a…

匹配式因果效应估计中的倾向得分增广AI 解读

When assessing the causal effect of a binary exposure using observational data, confounder imbalance across exposure arms must be addressed. Matching methods, including propensity score-based matching, can be used to deconfound the causal relationship of interest. They have been particularly popular in practice, at least in part due to their simplicity and interpretability. However, these methods can suffer from low statistical efficiency compared to many competing methods.…

存在干扰的线性情境老虎机AI 解读

Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in…

广义线性模型中异质性处理效应的惩罚在线序贯检验AI 解读

Identification of heterogeneous treatment effects (HTEs) has been increasingly popular and critical in various penalized strategy decisions using the A/B testing approach, especially in the scenario of a consecutive online collection of samples. However, in high-dimensional settings, such an identification remains challenging in the sense of lack of detection power of HTEs with insufficient sample instances for each batch sequentially collected online. In this article, a…

用于观察性剂量—反应推断的高斯过程AI 解读

We adapt Gaussian processes for estimating the average dose-response function in observational settings, introducing a powerful complement to treatment effect estimation for understanding heterogeneous effects. We incorporate samples from a Gaussian process posterior for the propensity score into a Gaussian process response model using Girard's approach to integrating over uncertainty in training data. We show Girard's method admits a positive-definite kernel, and provide…

多值时变处理下边际结构模型的非参数有效估计AI 解读

Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric estimator exist for marginal structural models with multi-valued and time-varying treatments. In this paper, we use machine learning together with recent developments in semiparametric efficiency theory for longitudinal studies to propose such an estimator. The proposed estimator is based on a study…

对不平等敏感的最优处理分配AI 解读

The egalitarian equivalent, $ee$, of a societal distribution of outcomes with mean $m$ is the outcome level such that the evaluator is indifferent between the distribution of outcomes and a society in which everyone obtains an outcome of $ee$. For an inequality averse evaluator, $ee < m$. In this paper, I extend the optimal treatment choice framework in Manski (2024) to the case where the welfare evaluation is made using egalitarian equivalent measures, and derive optimal…

处理效应随暴露时间或日历时间变化时的阶梯楔形整群随机试验分析AI 解读

Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the true underlying treatment effect varies by exposure time can produce severely misleading estimates. Alternatively, the true underlying treatment effect might vary by calendar time. Comparatively less work has examined treatment effect…

通过矩约束学习实现神经网络自动去偏AI 解读

Causal and nonparametric estimands in economics and biostatistics can often be viewed as the mean of a linear functional applied to an unknown outcome regression function. Naively learning the regression function and taking a sample mean of the target functional results in biased estimators, and a rich debiasing literature has developed where one additionally learns the so-called Riesz representer (RR) of the target estimand (targeted learning, double ML, automatic debiasing…

利用捕获—再捕获方法改进观察队列中的处理效应评估AI 解读

We extend recently proposed design-based capture-recapture (CRC) methods for prevalence estimation among registry participants, in order to enhance treatment effect evaluation among a trial-eligible target population. The so-called ``anchor stream design" for CRC analysis integrates an observational study cohort with a randomized trial involving a small representative study sample, and enhances the generalizability and transportability of CRC findings. We show that a novel…

通过因果子群发现估计生存结局的可解释异质性处理效应AI 解读

Estimating heterogeneous treatment effect (HTE) for survival outcomes has gained increasing attention, as it captures the variation in treatment efficacy across patients or subgroups in delaying disease progression. However, most existing methods focus on post-hoc subgroup identification rather than simultaneously estimating HTE and selecting relevant subgroups. In this paper, we propose an interpretable HTE estimation framework that integrates three meta-learners that…

面向设计型因果加权问题的表征学习AI 解读

Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting…

以噪声对抗噪声：大量候选工具变量下的因果推断AI 解读

Instrumental variable methods provide useful tools for inferring causal effects in the presence of unmeasured confounding. To apply these methods with large-scale data sets, a major challenge is to find valid instruments from a possibly large candidate set. In practice, most of the candidate instruments are often not relevant for studying a particular exposure of interest. Moreover, not all relevant candidate instruments are valid as they may directly influence the outcome…

大型 Probit 模型的高效计算估计AI 解读

Probit models are useful for modeling correlated discrete responses in many disciplines, including consumer choice data in economics and marketing. However, the Gaussian latent variable feature of probit models coupled with identification constraints pose significant computational challenges for its estimation and inference, especially when the dimension of the discrete response variable is large. In this paper, we propose a computationally efficient Expectation-Maximization…

最小稀疏性下含高维干扰函数的根号样本量一致半参数学习AI 解读

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome regression and the propensity score) have been developed in recent years. However, it is still unclear what is the…

矩阵值中介变量的贝叶斯联合中介分析模型AI 解读

Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs-at-risk (OARs), can inform future treatment planning. The radiation exposure to OARs can be summarized by a matrix of dose-volume histograms (DVH) for each patient. Although various methods for high-dimensional mediation analysis have…

函数型因果发现的诊断工具AI 解读

Causal discovery methods aim to determine the causal direction between variables using observational data. Functional causal discovery methods, such as those based on the Linear Non-Gaussian Acyclic Model (LiNGAM), rely on structural and distributional assumptions to infer the causal direction. However, approaches for assessing causal discovery methods' performance as a function of sample size or the impact of assumption violations, inevitable in real-world scenarios, are…

因果发现中的假设违反与得分匹配的稳健性AI 解读

When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their data. Because causal discovery without further assumptions is an ill-posed problem, each algorithm comes with its own set of usually untestable assumptions, some of which are hard to meet in real datasets. Motivated by these…

早产儿观察研究中连续工具变量的增强：匹配、部分识别界与推断AI 解读

Regionalization of intensive care for premature babies refers to a triage system of mothers with high-risk pregnancies to hospitals of varied capabilities based on risks faced by infants. Due to the limited capacity of high-level hospitals, which are equipped with advanced expertise to provide critical care, understanding the effect of delivering premature babies at such hospitals on infant mortality for different subgroups of high-risk mothers could facilitate the design of…

利用反事实删失无偏变换估计生存结局的异质性处理效应AI 解读

Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival outcomes and almost none to settings with competing risks. In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks. After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous…

利用多重先验强化随机对照试验并学习外部有效性AI 解读

This paper introduces a framework for incorporating prior information into the design of sequential experiments. These sources may include past experiments, expert opinions, or the experimenter's intuition. We model the problem using a multi-prior Bayesian approach, mapping each source to a Bayesian model and aggregating them based on posterior probabilities. Policies are evaluated on three criteria: learning the parameters of payoff distributions, the probability of…